Murrell, Paul (2009). Introduction to Data Technologies, London: Chapman & Hall/CRC.
20/09/2018
Welcome to Data Handling: I.C.V. 2018!
Introductory Example
Data input, processing, output
The Data Pipeline
Data Science workflow. Source: Wickham and Grolemund (2017)
The Data Pipeline
Data Science workflow. Source: Wickham and Grolemund (2017)
What could be the output of all this?
The Data Pipeline
- Research report/paper (e.g., BA Thesis)
- Presentation/Slides
- Website
- Web application (interactive; alas the introductory example)
- Dashboard for management
- Recommender system (i.e., a trained machine learning algorithm)
- …
‘Data Science’?
‘Data Science’?
“This coupling of scientific discovery and practice involves the collection, management, processing, analysis, visualization, and interpretation of vast amounts of heterogeneous data associated with a diverse array of scientific, translational, and inter-disciplinary applications.”
University of Michigan ‘Data Science Initiative’, 2015
But, what about Statistics?!
“Seemingly, statistics is being marginalized here; the implicit message is that statistics is a part of what goes on in data science but not a very big part. At the same time, many of the concrete descriptions of what the DSI will actually do will seem to statisticians to be bread-and-butter statistics. Statistics is apparently the word that dare not speak its name in connection with such an initiative!”
David Donoho (2015). 50 years of Data Science
The ‘Data Science Venn Diagram’. Source: http://berkeleysciencereview.com/how-to-become-a-data-scientist-before-you-graduate/
The ‘Data Science Venn Diagram’. Source: http://berkeleysciencereview.com/how-to-become-a-data-scientist-before-you-graduate/
Background
What’s new about all this?
“All in all, I have come to feel that my central interest is in data analysis, which I take to include, among other things: …”
What’s new about all this?
“All in all, I have come to feel that my central interest is in data analysis, which I take to include, among other things: procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data.”
What’s new about all this?
Technological change
Technological change
Source: https://techxerl.net.
Source: Source: statista.com.
Top: Number of mentions of the terms ‘Big Data’ or ‘Artificial Intelligence’ in academic and media sources, 1980-2016. Bottom: Number of mentions in The New York Times and The Wall Street Journal, used as proxies for U.S. mainstream media and business media. Note logarithmic y-axis scale. Source: Katz (2017).
Organization of the Course
Help Wanted
- Experienced R user?
- Assist fellow students during exercises in class
- Disclaimer: this is not an official TA position!
Help Wanted
- Experienced R user?
- Assist fellow students during exercises in class
- Disclaimer: this is not an official TA position!
- Interested?
- Approach me at the end of today’s lecture!
- Or send me an email: ulrich.matter@unisg.ch
Course evaluation: Focus Group
Course Structure
Course Concept
- Lectures (every Thursday morning)
- Background/Concepts
- Live demonstrations of concepts
- Illustration of ‘hands-on’ approaches
Course Concept
- Lectures (every Thursday morning)
- Background/Concepts
- Live demonstrations of concepts
- Illustration of ‘hands-on’ approaches
- Workshops/Exercises (bi-weekly evening sessions)
- Guided tutorials
- Discussion of homework exercises
- Recap of theoretical concepts
Course Concept
- Lectures (every Thursday morning)
- Background/Concepts
- Live demonstrations of concepts
- Illustration of ‘hands-on’ approaches
- Workshops/Exercises (bi-weekly evening sessions)
- Guided tutorials
- Discussion of homework exercises
- Recap of theoretical concepts
- First Exercises (set up R/RStudio) is available on StudyNet today
Course Concept
- Lectures (every Thursday morning)
- Background/Concepts
- Live demonstrations of concepts
- Illustration of ‘hands-on’ approaches
- Workshops/Exercises (bi-weekly evening sessions)
- Guided tutorials
- Discussion of homework exercises
- Recap of theoretical concepts
- First Exercises (set up R/RStudio) is available on StudyNet today
- Guest Lectures
Course Concept
- Strongly encouraged: Learning groups!
- Workshops/Exercises-Sessions will provide opportunity.
- Tackle the tricky exercises together!
15/11/2018: Guest Lecture by Dr. Michael Zehnder
Co-Founder & CEO Swiss Data Labs AG
Part I: Data (Science) Fundamentals
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| Date | Topic |
|---|---|
| 20.09.2018 | Introduction: Big Data/Data Science, course overview |
| 27.09.2018 | An introduction to data and data processing |
| 27.09.2018 | Exercises/Workshop 1: Tools, working with text files |
| 04.10.2018 | Data storage and data structures |
| 11.10.2018 | ’Big Data‘ from the Web |
| 11.10.2018 | Exercises/Workshop 2: Computer code and data storage |
Part II: Data Gathering and Preparation
| Date | Topic |
|---|---|
| 18.10.2018 | Programming with data |
| 25.10.2018 | Data sources, data gathering, data import |
| 25.10.2018 | Exercises/Workshop 3: Programming with Data |
| 15.11.2018 | Guest Lecture: Dr. Michael Zehnder (Swiss Data Labs, gateB) |
| 22.11.2018 | Data preparation and manipulation |
| 22.11.2018 | Exercises/Workshop 4: Data import and data preparation/manipulation |
| 29.11.2018 | Research Insights: The Programmable Web, Big Public Data, and Political Economics |
Part III: Analysis, Visualisation, Output
| Date | Topic |
|---|---|
| 06.12.2018 | Basic statistics and data analysis with R |
| 06.12.2018 | Exercises/Workshop 5: Applied data analysis with R |
| 13.12.2018 | Visualization, dynamic documents |
| 20.12.2018 | Exercises/Workshop 6: Visualization, dynamic documents; Wrap-Up, Q&A |
| 20.12.2018 | Exam Exchange Students |
Core Course Resources
- Course materials: umatter.github.io/courses
- Exercises will be uploaded to the StudyNet room!
Main textbooks
Further resources
Exam Information
- Central, written examination.
- Multiple choice questions.
- Theoretical concepts and practical applications in R (questions based on code examples).
Exam Information II
- Exercises towards the end of the term will contain sample questions.
- Get familiar with style of questions.
- Exchange students who need to take the exam before the central exam block:
- Notify me until end of September: ulrich.matter@unisg.ch!
- Option for a decentral exam during the term (probably the day of the last lecture).
Q&A
References
Katz, Yarden. 2017. “Manufacturing an Artificial Intelligence Revolution.” https://ssrn.com/abstract=3078224.
Wickham, Hadley, and Garrett Grolemund. 2017. Sebastopol, CA: O’Reilly. http://r4ds.had.co.nz/.